Wenji Li’s research while affiliated with Shantou University and other places

This study addresses dynamic task allocation challenges in coordinated surveillance involving multiple unmanned aerial vehicles (UAVs). A significant concern is the increased UAV flight distance resulting from the assignment of new missions, leading to decreased reconnaissance efficiency. To tackle this issue, we introduce a collaborative multi-target and multi-UAV reconnaissance scheme. Initially, the multitasking constrained multi-objective optimization framework (MTCOM) is employed to optimize task allocation and reconnaissance time in static scenarios. Subsequently, in case of emergency, we iteratively refine the outcomes of static task allocation through an enhanced auction-based distributed algorithm, effectively reducing UAV flight costs in response to new missions, UAV withdrawal, or damage. Simulation results demonstrate the efficacy of our proposed multi-UAV and multi-target cooperative reconnaissance scheme in resolving dynamic task allocation issues. Additionally, our approach achieves a 5.4% reduction in UAV flight distance compared to traditional allocation methods. The main contribution of this paper is to consider a dynamic scenario model involving UAV damage and the emergence of new reconnaissance areas. Then we propose an innovative collaborative multi-target and multi-UAV reconnaissance scheme to address this issue and, finally, conduct experimental simulations to verify the effectiveness of the algorithm.

Design automation is a core technology in industrial design software and an important branch of knowledge-worker automation. For example, electronic design automation (EDA) has played an important role in both academia and industry. Design automation for intelligent robots refers to the construction of unified modular graph models for the morphologies (body), controllers (brain), and vision systems (eye) of intelligent robots under digital twin architectures, which effectively supports the automation of the morphology, controller, and vision system design processes of intelligent robots by taking advantage of the powerful capabilities of genetic programming, evolutionary computation, deep learning, reinforcement learning, and causal reasoning in model representation, optimization, perception, decision making, and reasoning. Compared with traditional design methods, MOdular DEsigN Automation (MODENA) methods can significantly improve the design efficiency and performance of robots, effectively avoiding the repetitive trial-and-error processes of traditional design methods, and promoting automatic discovery of innovative designs. Thus, it is of considerable research significance to study MODENA methods for intelligent robots. To this end, this paper provides a systematic and comprehensive overview of applying MODENA in intelligent robots, analyzes the current problems and challenges in the field, and provides an outlook for future research. First, the design automation for the robot morphologies and controllers is reviewed, individually, with automated design of control strategies for swarm robots also discussed, which has emerged as a prominent research focus recently. Next, the integrated design automation of both the morphologies and controllers for robotic systems is presented. Then, the design automation of the vision systems of intelligent robots is summarized when vision systems have become one of the most important modules for intelligent robotic systems. Then, the future research trends of integrated “Body-Brain-Eye” design automation for intelligent robots are discussed. Finally, the common key technologies, research challenges and opportunities in MODENA for intelligent robots are summarized.

During the daily operation of photovoltaic array, it easily faces the partial shading conditions resulted from the cloud shadow, dropping dust, etc. It will directly cause a lifetime reduction and a generation efficiency decrement for the photovoltaic array. To weaken the negative influence of partial shading condition, one of the most favoured ways is the photovoltaic array reconfiguration. However, the conventional photovoltaic array reconfiguration only aims to maximize the power output, which did not consider the lifetime and control complexity of switching devices. To fill up this gap, this paper constructs a new bi-objective optimization of photovoltaic array reconfiguration, which attempts to simultaneously maximize the output power and minimize the switching number. Consequently, it can dramatically reduce the switching control complexity while improving the generation efficiency, while the operation life of the switching devices can be lengthened. In order to find a high-quality Pareto optimal reconfiguration schemes, six frequently-used evolutionary multi-objective optimization algorithms are employed to solve this bi-objective optimization. The effectiveness of bi-objective optimization of photovoltaic array reconfiguration is tested on three scales of total-cross-tied photovoltaic arrays under four partial shading patterns. The simulation results show that the maximum power increment by the proposed technique is up to 26.6% against to that without optimization, while the average switch number decrement is up to 31.1% compared with the single-objective optimization algorithms.

In recent years, many effective constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed and successfully applied to address constrained multi-objective optimization problems (CMOPs). Nevertheless, few CMOEAs have fully explored CMOPs with imbalanced objectives or constraints. In this study, we propose a hybrid algorithm called M2M-IEpsilon to handle CMOPs with such characteristics, which combines an improved epsilon constraint-handling method (IEpsilon) with a multi-objective to multi-objective (M2M) decomposition strategy. The M2M decomposition mechanism divides a population into a set of sub-populations, which strengthens the diversity of the population. The IEpsilon constraint-handling method enables individuals with small constraint violation values to survive to the next generation, thus leading to a search for promising regions. In addition, a series of imbalanced CMOPs, named IM-CMOPs, is designed to verify the performance of the proposed M2M-IEpsilon algorithm. The comprehensive experimental results indicate that the proposed method can solve such imbalanced CMOPs well and perform significantly better than the other eight new or classical CMOEAs. Finally, we used the proposed M2M-IEpsilon to optimize the wellbore trajectory problem, and it achieved good results compared with previously developed algorithms.

The hard rock Tunnel Boring Machine (TBM) is a complex engineering equipment coupled with multiple sub-systems for underground tunnel excavation in complex geological environments. Resetting the operational and structural parameters of TBM according to different geological conditions usually requires engineers to spend a lot of time dealing with the interaction between various subsystems, which is a tedious and time-consuming job. To facilitate setting the operational and structural parameters of TBM, we present a constrained multi-objective optimization model and its solving method in this paper. To be specific, three performance indices, i.e. minimizing the system construction period, construction energy consumption and construction cost of TBM, are firstly considered as the three objectives in the proposed model. Secondly, two push and pull search(PPS) based algorithms, including PPS-MOEA/D and PPS-KnEA, are suggested to solve the formulated constrained multi-objective optimization problem. Finally, to verify the performance of the developed method, the presented method is compared with several popular constrained multi-objective evolutionary algorithms by tackling the established optimization model. The experimental results reveal that the presented method has the best performance among the comparison algorithms, and the overall performance of the algorithm with PPS is better than other algorithms without PPS, which indicates the superiority of PPS framework in solving practical optimization problems.

Recently, many methods based on hand-designed convolutional neural networks (CNNs) have achieved promising results in automatic retinal vessel segmentation. However, these CNNs remain constrained in capturing retinal vessels in complex fundus images. To improve their segmentation performance, these CNNs tend to have many parameters, which may lead to overfitting and high computational complexity. Moreover, the manual design of competitive CNNs is time-consuming and requires extensive empirical knowledge. Herein, a novel automated design method, called Genetic U-Net, is proposed to generate a U-shaped CNN that can achieve better retinal vessel segmentation but with fewer architecture-based parameters, thereby addressing the above issues. First, we devised a condensed but flexible search space based on a U-shaped encoder-decoder. Then, we used an improved genetic algorithm to identify better-performing architectures in the search space and investigated the possibility of finding a superior network architecture with fewer parameters. The experimental results show that the architecture obtained using the proposed method offered a superior performance with less than 1% of the number of the original U-Net parameters in particular and with significantly fewer parameters than other state-of-the-art models. Furthermore, through in-depth investigation of the experimental results, several effective operations and patterns of networks to generate superior retinal vessel segmentations were identified. The codes of this work are available at https://github.com/96jhwei/Genetic-U-Net .